In agent systems, context matters more than the prompt
A practical guide to what the planner actually sees, why context is more than the prompt, and why better context selection matters more than simply adding more tokens.
A practical guide to what the planner actually sees, why context is more than the prompt, and why better context selection matters more than simply adding more tokens.
I already had reusable `uv`-first instructions for Claude Code and Codex. I have now added the Cursor path too: generate a rule from the same source guidance so Cursor stays on the same Python workflow.
A practical guide to what agents are, what they are not, and how to reason about them in terms of context, tools, host loops, and runtime constraints instead of hype.
I kept noticing coding agents drifting into `pip install`, raw `python`, and hand-edited dependency files in Python projects. So I built a small repo with reusable `CLAUDE.md` and `AGENTS.md` guidelines that push both Claude and Codex toward a consistent `uv` workflow.
Anthropic just shipped native git worktree support in Claude Code CLI. Last week I wrote about a hub-and-spoke worktree setup for parallel AI agents --- now it's getting baked right into the tool.